Prescription Fraud detection via data mining : a methodology proposal

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- -Bilkent University, 2009.Includes bibliographical references leaves 61-69Fraud is the illegitimate act of violating regulations in order to gain personal profit. These kinds of violations are seen in many important areas including, healthcare, computer networks, credit card transactions and communications. Every year health care fraud causes considerable amount of losses to Social Security Agencies and Insurance Companies in many countries including Turkey and USA. This kind of crime is often seem victimless by the committers, nonetheless the fraudulent chain between pharmaceutical companies, health care providers, patients and pharmacies not only damage the health care system with the financial burden but also greatly hinders the health care system to provide legitimate patients with quality health care. One of the biggest issues related with health care fraud is the prescription fraud. This thesis aims to identify a data mining methodology in order to detect fraudulent prescriptions in a large prescription database, which is a task traditionally conducted by human experts. For this purpose, we have developed a customized data-mining model for the prescription fraud detection. We employ data mining methodologies for assigning a risk score to prescriptions regarding Prescribed Medicament- Diagnosis consistency, Prescribed Medicaments’ consistency within a prescription, Prescribed Medicament- Age and Sex consistency and Diagnosis- Cost consistency. Our proposed model has been tested on real world data. The results we obtained from our experimentations reveal that the proposed model works considerably well for the prescription fraud detection problem with a 77.4% true positive rate. We conclude that incorporating such a system in Social Security Agencies would radically decrease human-expert auditing costs and efficiency.Aral, Karca DuruM.S

Implementing US-style anti-fraud laws in the Australian pharmaceutical and health care industries

This article critically analyses the prospects for introducing United States anti-fraud (or anti-false claims) laws in the Australian health care setting. Australian governments spend billions of dollars each year on medicines and health care. A recent report estimates that the money lost to corporate fraud in Australia is growing at an annual rate of 7%, but that only a third of the losses are currently being detected. In the US, qui tam provisions - the component of anti-fraud or anti-false claims laws involving payments to whistleblowers - have been particularly successful in providing critical evidence allowing public prosecutors to recover damages for fraud and false claims made by corporations in relation to federal and state health care programs. The US continues to strengthen such anti-fraud measures and to successfully apply them to a widening range of areas involving large public investment. Australia still suffers from the absence of any comprehensive scheme that not only allows treble damages recovery for fraud on the public purse, but crucially supports such actions by providing financial encouragement for whistleblowing corporate insiders to expose evidence of fraud. Potential areas of application could include direct and indirect government expenditure on health care service provision, pharmaceuticals, medical devices, defence, carbon emissions compensation and tobacco-related illness. The creation in Australia of an equivalent to US anti-false claims legislation should be a policy priority, particularly in a period of financial stringency

Punishing Pharmaceutical Companies for Unlawful Promotion of Approved Drugs: Why the False Claims Act is the Wrong Rx

This article criticizes the shift in focus from correction and compliance to punishment of pharmaceutical companies allegedly violating the Food, Drug, & Cosmetic Act (FD&C Act) prohibitions on unlawful drug promotion. Traditionally, the Food and Drug Administration (FDA) has addressed unlawful promotional activities under the misbranding and new drug provisions of the FD&C Act. Recently though, the Justice Department (DOJ) has expanded the purview of the False Claims Act to include the same allegedly unlawful behavior on the theory that unlawful promotion “induces” physicians to prescribe drugs that result in the filing of false claims for reimbursement. Unchecked and unchallenged, the DOJ has negotiated criminal and civil settlements with individual pharmaceutical companies ranging from just under ten to hundreds of millions of dollars. In part, companies settle these cases to avoid the potential loss of revenue associated with the exclusion regime administered by the U.S. Department of Health and Human Services, under which companies risk losing the right to participate in federal health care programs. Even more disturbing, these settlements allow DOJ to circumvent judicial review of its enforcement approach, preventing any type of accountability for its legal theories or procedures. This article discusses the traditional enforcement methods employed by the FDA as well as the more recent DOJ prosecutions under the False Claims Act. Although it concludes that the FD&C Act should provide the sole means for prosecuting unlawful drug promotion, it also suggests that when prosecuting pharmaceutical companies under either Act, the government must avoid the temptation to mine companies for large settlements in lieu of developing a more coherent and responsible enforcement strategy

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Show Me Your Claims and I\u27ll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

Type of Tomato Classification Using Deep Learning

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task